System and method for fashion recommendations

ABSTRACT

Novel system, methods, which include machine learning, and device for providing color and fashion recommendations, including for persons with visual impairment such as color blindness or complete blindness. Also, methods providing a data storage system for storing digital renditions of garments; providing a portable communication device to extract color and/or pattern from garments through use of a camera and at least one algorithm; providing a processor capable of accessing locally stored and/or remote information about or learning the preferred matching set of garments; assigning each garment in the set of garments a red-green-blue (RGB) value; providing a suitability ranking for matching compatibility of the garment or the set of garments; and providing recommendations for preferred matching garment or set of garments by organizing the garments in at least one queue selected from the group consisting of audial, tactile, visual or a combination thereof, wherein the individual imports garments or set of garments, through a series of photos or video, for bulk import into a virtual closet for the identification and assignment of type of garments or set of garments using human or computational methods.

RELATED APPLICATIONS

This application is a continuation-in-part application based on U.S.patent application Ser. No. 17/559,548 filed Dec. 22, 2021, which is acontinuation-in-part based on U.S. patent application Ser. No.17/499,106 filed Oct. 12, 2021, now U.S. Pat. No. 11,328,339, which is acontinuation-in-part based on U.S. patent application Ser. No.17/174,031 filed Feb. 11, 2021, now U.S. Pat. No. 11,157,988, which is acontinuation-in-part application of U.S. patent application Ser. No.16/842,247 filed Apr. 7, 2020, now U.S. Pat. No. 10,963,944, which is adivisional application of U.S. patent application Ser. No. 15/826,245filed Nov. 29, 2017, now U.S. Pat. No. 10,614,506, which claims thebenefit of priority from U.S. Provisional Patent Application No.62/451,592 filed Jan. 27, 2017, the entire contents of which are hereinincorporated by reference.

FIELD OF THE INVENTION

The present invention provides a novel system, methods, which includemachine learning, and device for providing color and fashionrecommendations, including for persons with visual impairment such ascolor blindness or complete blindness. Many online or application-basedfashion systems provide personal image content that allow users toselect and evaluate combinations of clothes by eye. Some of thesesystems include outside human expert guidance as to what is or is notfashionable. A present challenge is to assist color blind or blindpeople, or people with little to no fashion sense, with a system,methods, and device that provides the ability to evaluate clothingoptions from their closet, other people's closets, retailers, orelsewhere, record and plan clothing sets for future engagements, andreceive audio, video, or other means of feedback about theappropriateness of those selections. The present invention addressesthese challenges to help evaluate and recommend color and fashionsolutions.

BACKGROUND OF THE INVENTION

For both men and women, fashion styles, as well as the clothing itemsincludes in such styles, may be very difficult to distinguish anddiscern. It can be even more so for certain individuals having specificeye conditions, such as color blindness.

It is known in the relevant art that attempts to solve such a problemrelated to selection based on fashion choices exist. For example, U.S.Pat. No. 8,249,941 describes methods for selecting fashion items,pairing with additional fashion items, as associated with a stylematrix. However, this is a system based on a static fashion style matrixwith set attributes, as opposed to accessing any other type of variable(such as opinions of fashion experts, etc.). Further, there is noassociation with actual clothing items within a user's wardrobe.

U.S. Pat. No. 8,103,551 discloses methods using a mathematical functionto ascertain any similarities between article attributes and compares toa threshold in order to determine if such attributes are sufficientlysimilar. While such methods are helpful in comparing articles ofclothing in order to recognize associations across different types ofarticles, there is no ability to incorporate other variables intomethodology to include more practical items, such as digital versions ofan actual user's clothes in their closet, or inclusion of expertrecommendations for subsequent matching of clothing or real-time accessto purchase such outfits.

Thus, there still exists a need in the art to help evaluate andrecommend color and fashion solutions on a device suitable for a user inneed thereof.

SUMMARY OF THE INVENTION

This invention provides a system, methods, and device for optimizingcolor and fashion decisions for visually impaired and other people. Theinvention allows the human user to more competently choose clothing setsin relation to season, weather, or other environment. The system,methods, and device also provides the opportunity to learn about usefulrecommendations from the data collected through a social network ofother human users, coupled with knowledge about apparel manufacturersand their available garments and costs.

In one aspect, the present invention provides a system of selecting apreferred matching set of garments to assemble an outfit, comprising: adevice further comprising a data storage system for storing the set ofgarments, wherein the set of garments further comprises colors and/orpatterns as inputted into the device by a user, wherein the set ofgarments is scored by the device.

In another aspect, the present invention describes a method of selectinga preferred matching set of garments to assemble an outfit for anindividual, the method comprising: providing a data storage system forstoring digital renditions of garments or accessories, providing aprocessor capable of accessing locally stored and/or remote informationabout or learning the preferred matching set of garments.

BRIEF DESCRIPTION OF THE DRAWINGS

The novel features of this invention, as well as the invention itself,both as to its structure and its operation, will be best understood fromthe accompanying drawings, taken in conjunction with the accompanyingdescription, in which similar reference characters refer to similarparts, and in which:

FIG. 1 depicts a block diagram of the system, methods, and device forproviding fashions recommendations to users.

FIG. 2 depicts a block diagram of the system and methods for the inputand use of clothing items within the device.

FIG. 3 depicts a block diagram of the system and methods for use ofscoring and providing scores for clothing sets and providing informationback to retailers within the device.

FIG. 4 depicts a block diagram of the system and methods allowing foruser-to-user communication and sharing of closets and clothes throughthe device.

FIG. 5 depicts a block diagram of the system and methods for thecollection of data by type and their utility to retailers within thedevice.

FIG. 6 depicts a block diagram of the system and methods of populatingvirtual closets using various means with the device.

FIG. 7 depicts a block diagram of the system and methods allowing theassignment of proper labels to colors in the device.

FIG. 8 depicts a block diagram of the system and methods for use bythird party entities, including garment cleaning companies.

FIG. 9 depicts a block diagram of the system and methods allowing theuse of virtual closets for garments in virtual settings.

FIG. 10 depicts a block diagram of the system and methods for thedigital design of novel digital garment representations using a designengine.

FIG. 11 depicts a block diagram of the system and methods allowing theinclusion of skin tone and makeup for the generation and scoring ofoutfits, as well as in the design of novel digital representations ofthe physical world.

REFERENCE NUMERALS IN DRAWINGS

Reference Numerals in FIG. 1 :

-   100 Set of users-   102 User-   104 Portable communication device-   106 Software application-   108 Preferences and constraints-   110 Measurements-   112 Virtual closet-   114 Tagged attributes-   116 Calendar-   118 Forecasted weather-   120 Event setting and lighting-   122 Recommendation system-   124 Best set of clothes-   126 Fashion expert-   128 Social network-   130 Expert system-   132 Machine learning algorithm-   134 Visual, audial, tactile queues-   136 Alerts to user

Reference Numerals in FIG. 2 :

-   200 Hand-held mobile phone-   202 Software application-   204 Collections of previously saved clothing articles-   206 View individual saved clothing items-   208 Enter new clothing items into a virtual closet-   210 Identify users for clothes sharing-   212 Assemble new outfits and receive recommended outfit ratings-   214 Ratings generator-   216 Modifications by system administrator

Reference Numerals in FIG. 3 :

-   300 Machine learning methods-   302 Previous user likes-   304 Preferred user outfits-   306 Generalization of future user likes-   308 Clothing features-   310 Generalization over multiple users-   312 Numeric rating-   314 Non-numeric rating-   316 Feedback from the user and/or other users-   318 Retailers-   320 Sales trends-   322 Mathematical model for new clothes to acquire

Reference Numerals in FIG. 4 :

-   400 User communication-   402 Identify clothing for possible sharing, borrowing, or sale-   404 View virtual closets of other users-   406 Identify articles of clothing available to other users

Reference Numerals in FIG. 5 :

-   500 Collection of data on the use of clothing by users-   502 Collection of data on user purchasing habits-   504 Collection of user demographic information-   506 Retailers

Reference Numerals in FIG. 6 :

-   600 Photos of clothing items-   602 Virtual closet-   604 Wireframes of articles of clothing-   606 User verification of suitability-   608 Determine luminosity and adjust RGB color-   610 Photo with current light setting as reference

Reference Numerals in FIG. 7 :

-   700 Various colors-   702 Commonly known colors-   704 Add labels to colors of interest

Reference Numerals in FIG. 8 :

-   800 Users-   802 User virtual closet-   804 Garment cleaning companies-   806 Virtual closets with garments-   808 Bulk import-   810 Third party entity virtual closet-   812 Garment tracking-   814 User notifications-   816 User personal calendar-   818 Garment deterioration-   820 Multiple user virtual closets-   822 Garment renting or sharing-   824 Garment status-   826 Trend analysis

Reference Numerals in FIG. 9 :

-   900 User physical presence-   902 User virtual presence-   904 Software application-   906 Physical closet-   908 Virtual closet-   910 Digital renditions-   912 Color matching-   914 Design engine-   916 Non-fungible token garment designs-   918 Generate new digital garments-   920 Digital versions of popular garments-   922 Database of digital garment designs-   924 Designers and retailers-   926 Physically wearable garments-   928 Digital gaming environment-   930 Digital garments on virtual characters-   932 Evaluation in digital context(s)

Reference Numerals in FIG. 10 :

-   1000 Design engine-   1002 Digital environment-   1004 Novel digital garment representations-   1006 RGB-based color palette-   1008 Pantone color palette

Reference Numerals in FIG. 11 :

-   1100 Users-   1102 Garment photos-   1104 Virtual closet-   1106 Makeup-   1108 Photo of user's face-   1110 Color palette-   1112 Human skin tones-   1114 Other facial colors-   1116 Computing device-   1118 Automatic recognition of facial features and colors-   1120 Generation of outfits-   1122 Other fashion elements-   1124 Generation and scoring of outfits-   1126 Database of outfit information-   1128 Database of digital outfit information-   1130 New garment or makeup choices-   1132 Machine learning for trend analysis

DETAILED DESCRIPTION OF THE INVENTION

A preferred embodiment of the present invention is illustrated in FIG. 1.

A set of users 100 use the color and/or fashion recommendation system. Auser 102 interacts with the recommendation system using a computingdevice 104, most typically a hand-held phone. Residing on the phone is asoftware application 106 that is the basis of the recommendation system.The user inputs color and/or fashion preferences and constraints 108,measurements 110, and generates a virtual closet of their garments,potentially including accessories 112, where each garment or accessoryis associated with tagged attributes 114. A calendar 116 is used by theuser to define current and future events, their type, and garment needrelative to expected weather forecast 118, if known, and event settingand lighting 120. Within the device, a recommendation system 122 usesone or a set of approaches, to generate a suggested set of clothes 124for each event on the user's calendar 116. These approaches include theadvice of a human fashion expert 126, advice and/or choices of a socialnetwork of other users of the device and system 128, computer rule-basedand/or case-based expert system 130, and/or machine learning algorithm132. The suggested set of clothes is offered to the user using visual,audial, and/or tactile queues 134 where the user can approve or rejectthe recommendations. The user can iteratively use the system to findincreasingly appropriate sets of clothes. The user-selected set ofclothes is then automatically linked to the calendar, and the systemprovides alerts to the user 136 as to which sets to wear on which futuredates and which garments might be required to be picked up from thecleaners so that they are available in advance of specific events. Theoverall flow of the recommendation system, methods, and device allowsfor the rapid, interactive, selection of appropriate garments and/oraccessories with human assistance or using machine learning methods, andin a manner that can be compatible with colorblind or blind users aswell those who are non-visually impaired.

A user 102 from a set of users 100 makes use of a computing device 104such as a hand-held phone, tablet, personal computer, or personal dataassistant to interface with the software application 106 of interest.The user establishes color and/or fashion preferences and constraints108 that include, for example location, geocultural predisposition,personal measurements including body type, age, complexion, gender, haircolor, style, season, retailer, alerts, and the ability to link to otherusers of the software application. The choice of gender in thepreferences may cause changes to the manner in which information ispresented in the software application to the end user. The userestablishes measurements 110 for their size, shape, complexion, featuresand any other defining elements. These defining elements can alsoinclude qualitative or quantitative measures of the way in whichclothing and accessories fit on the user given the user's shape and sizemay change with time. These features and constraints may be used by thesystem, methods, and device to help the user review and score clothingsets that are considered appropriate or best. For example, the systemcan help the user determine which clothes are best on an internationaltrip where the chosen sets of clothes meet the appropriate color and/orfashion social conventions or forecasted weather at those foreignlocations.

The user establishes the clothing and/or accessories they own as avirtual closet 112 within the software application. This can beaccomplished by taking pictures of each item and importing them to thedevice, or in the preferred embodiment through the use of a processinternal to the system and device that generates virtual clothing thatis similar enough to the real clothing in color, quality, and shape soas to avoid the need to have to import photos of real items. Photos ofclothes can also be imported to the system, method, and device fromthird-party retailers that already have photos of clothing. All clothingin the virtual closet is tagged 114 with attributes that can be used toidentify the clothing in useful ways. For instance, the type of weatherthat is associated with the garment, the layer of the garment as forinstance underwear or outerwear, or any requirements associated withevents that are indoor or outdoor and associated lighting difference.Given this process is likely to be time-intensive for the end user, inthe preferred embodiment the attributes of garments are inferredautomatically through the history of their use and through the manner inwhich the user utilizes the garments over time. Under conditions oflimited space, such as loading baggage for a trip, the dimensions of thebaggage may provide an upper bound to the number of possible objectsselected, and this too can be established if desired in the preferencesand constraints 108 information. Each user can choose to share his/hervirtual closet with other users or choose which garments or accessoriesare specifically to be shared or not and with which specified users. Themore information that is shared between the users, the more informationthat can be used by the system, methods, and device towardsautomatically assigning proper attributes to garments. This informationis stored within the system, methods, and device and/or in connection toa cloud-computing environment. A central problem that the system,methods, and device solves for the user, is that the possiblecombinations of garments relative to dynamic schedules and constraintsare tedious to search by hand. The system, methods, and device allow theuser to explore these search spaces far more efficiently than everbefore.

The user may establish a calendar 116 within the software applicationfor the purpose of scheduling sets of clothing and accessories forcurrent or future events. These events are entered by the user or can beautomatically populated in accordance with events like nationalholidays, weekends vs. weekdays, and so forth. Or for instance an eventcould consist of a series of days linked together such as a businesstrip. Using the system, methods, and device, it is possible to structureclothing and accessory sets in advance for specific event types orlocations.

Within the user preferences 108, the user may choose to share theirentire calendar of events and associated clothing and/or accessory setswith other users or the user may set these requirements for each eventas they are entered into the calendar. The calendar can also beassociated with one of many types of electronic calendars oftenassociated with hand-held phones such as Microsoft Outlook or GoogleCalendar to populate the calendar internal to the system, methods, anddevice invention here.

Information from the preferences and constraints 108, measurements 110,virtual closet 112, clothing tags 114, calendar of events 116,forecasted weather at the event location 118, event setting and lighting120 are provided to a recommendation system 122 that associates sets ofclothes and/or accessories with specific future events and providesrecommendations for sets of clothes 124 back to the user. Therecommendation system can be in the form of opinion from a fashionexpert 126 or via a social network of users 128 or via rule-based and/orcase-based expert system 130 and/or an intelligent machine learningalgorithm 132 that learns over the stored history of calendarinformation and clothes worn by each day what the user may feel issuitable for a specific event. In the preferred embodiment the systemnot only uses the history of one user's fashion choices but the historyof all similar users using the system, methods, and device to recommendcolor and/or fashion choices. The learning is also associated withfeedback from the user as to which recommendations are preferred or not,or which sets worked well or not for each event on the calendar. In thepreferred embodiment the recommendation system is also associated withclothing retailers and their offerings such that the user can beprovided with a recommendation from the retailer or one that includesgarments or accessories from a retailer with an option to buy directlyfrom that retailer online, perhaps in advance of an event.

In the preferred embodiment, the suggested sets of clothing areavailable to the user using visual and/or audial and/or tactile queues134 such that the clothes represented in the system can be understood byany user, even those visually impaired by color blindness or completeblindness. These queues could be specific to the garment, as in “redshirt,” or specific to the quality of a set of clothes and accessoriesfor a specific event, as in “this set looks great,” or with a vibrationof the phone representing “success.” Once selected by the user as a setof clothes to wear to a specific event, that choice is automaticallyexported to the calendar 116 function associated with a specific datefor that event. As the calendar function maintains knowledge of allfuture event needs and which clothes are or are not available in theuser closet 112, recommendations can be provided back to the user toschedule cleaning services or offer alerts to the user 136 to ensurethat the clothes for a specific set will be available in time for aspecific event. Further, in the preferred embodiment the reservation ofspecific clothes for specific events may remove their availability forother functions at that same time. Some user events may require extendeddays, such as a vacation or business trip. In these cases the user canuse the application to choose sets of clothes for specific meetings orpurposes over that entire trip, in light of the size and weightconstraints of luggage or other concerns. The device can be used to makesure that garment sets are complete prior to an event as a checklist.For recurring events, such as a weekly sporting event where the user isa player on the team, alerts may be provided to ensure that all requiredaccessories are available and that the set is complete prior to leavingthe house for the game.

Suggested sets of clothing may also be recommended in light of thespecific background of interest. For example, for a formal function theuser may wish to wear a set of clothing and accessories that is best inlight of the background scenery at that function. The set of clothes forthe function could be determined in the absence of the backgroundinformation or, in the case where a photo of the background can beprovided in advance, the set of clothes and accessories could berecommended that suits the specific background.

The user may also choose to select a specific first article of clothingfrom the virtual closet and have the system, methods, and devicerecommend other clothes to complete a clothes set. The user can then usethe system, methods, and device to locate said other articles ofclothing from their real-world closet using the camera function on thedevice to identify clothing with similar characteristics as thoserecommended by the system, methods, and device. The identification couldbe on the basis of matching color, shape, size, pattern, or other visualcues. This ability helps users that are visually impaired in theidentification of garments that complete sets from their real closet.

In light of clothing set choices, in the preferred embodiment the userand/or the system itself then has the ability to score each set todetermine to determine their suitability. Clothing sets are then rankedin order of decreasing suitability, with a user defined set of clothingsets selected to be removed. Articles of clothing in the remainingclothing sets are used to generate new clothing set choices for the userin light of the many possible combinations and constraints definedpreviously. The user may iterate through this process of selection andvariation as a form of iterative interactive evolution. This process isrepeated until a garment set of sufficient worth to the user isidentified, and that set is added to the calendar for a specificfunction.

In the preferred embodiment the software allows for multiple users toshare the information about their virtual closets and calendars ofavailability such that the virtual closets of all users are shared. Thisincreases the likelihood that each user will be able to find newcombinations of clothes and/or accessories that are right for his/herfuture events. Users can choose to share his/her clothes and/oraccessories with or without a cost to other users. Additionally, theability for multiple users to share their clothing choices offers theopportunity for users to make sure that they do or do not wear the sameclothing sets to the same event as desired. The invention also allowsfor the opportunity for users to review and exchange clothing betweentheir real closets using the information from the virtual closets.

Through the knowledge of which clothes in the user closet are used mostoften, it is possible to determine favorite garments and/or accessoriesand also garments and/or accessories that are hardly ever used andtherefore provide an opportunity for removal from the closet either as adonation, or for sale, or for rent or loan to other users through thesystem. Through the system, methods, and device it is also possible fortwo or more users to share their profiles and virtual closets andevaluate sets of clothes and/or accessories to determine which sets lookbest on which users.

All data over all users in the system are stored, including the choicesof garments and accessories for events, the locations of those events interms of their geography and customs, timing, and so forth. These dataare in turn used to help users make improved clothing set choices withincreased efficacy and efficiency, and can also be used to helpretailers understand patterns in color and/or fashion behavior over alarge set of users over time. This helps retailers determine which typesof apparel to offer by time, season, activity, location, and price. Itis also envisioned in the preferred embodiment that retailers would payto advertise their goods through the system, methods, and device tousers who are most inclined to be interested in buying their garmentsand/or accessories to complete clothing sets. In another instance of thepreferred embodiment, cleaning services pay access to use the system anddevice to determine which users are located near their service center,and which clothes they are likely to need cleaned at what times. Thisimproves the opportunity for cleaning services to schedule anddistribute their load and provide incentives to users to use theirservices.

In a preferred embodiment, the user opens the system, methods, anddevice using a software application (app) on a hand-held mobile phone200. The user enters his/her account in the app 202, which then providesthe opportunity for the user to (1) access saved outfits, which arecollections of clothing articles that the user has saved on prioroccasions 204, (2) view individual saved clothing items, 206 (3) enternew clothing items into a virtual closet in the app using either photostaken by the user or other graphics, 208 (4) identify other users toshare clothes with or otherwise communicate with, 210 (5) assemble newoutfits and receive recommended ratings of the outfits as they are beingassembled 212. Other features may also be incorporated.

In a preferred embodiment when the user wants to assemble a new outfit,if the user is a man, he starts by selecting a shirt. The user selectsthe type of shirt, such as dress shirt, polo/casual collared shirt, ort-shirt. Based on the type of selection, the remaining types of clothesthat can be selected is constrained by the system to be appropriate tothe type of clothes selected already. For example, if the user selects adress shirt, then the user cannot add a polo shirt or t-shirt to theoutfit because the user already has a shirt. In addition, if the userselects a dress shirt, then the user is offered the possibility toselect dress pants but not short pants.

At each point in assembling an outfit, which in a preferred embodimentconsists of at least a selected shirt and selected pants, the systemprovides ratings for how well the articles of clothes go together 214.These ratings can be provided based on (1) expert opinion, (2) theopinions of other users (3) a rule-based or case-based expert system,and/or (4) machine learning. In the case of providing an expert opinion,the opinion of an expert on how well the clothes go together may bepreloaded in the app or may be generated in real-time based on animmediate expert response from someone knowledgeable about colormatching and/or fashion choices. The same is true when considering theopinions of other users, which may be given in real-time or at a latertime.

In the case of a rule-based or case-based expert system, in a preferredembodiment, the app uses a set of decision trees to determine the ratingfor each next article of clothing based on the articles alreadyselected. Ratings are provided on a scale, such as a star scale, with 1star being low and 5 stars being high or vice versa. The ratings aredetermined based on how well the colors and/or styles of the clothescomplement each other, as well potentially by the date, time, and placethat they are expected to be worn based on the user's entry in acalendar. The decision trees in the app can be updated over time by asystem administrator 216 who retains authority to modify the app for anyor all users.

In the case of machine learning, in a preferred embodiment the app usesmachine learning techniques, which may include a combination of neuralnetworks or deep learning (neural networks with more than one hiddenlayer), evolutionary algorithms, reinforcement learning, support vectormachines, random forest methods, swarm optimization, fuzzy logic, andother techniques. The machine learning methods 300 use data on what theuser has liked previously 302, based on what the user has saved aspreferred outfits 304, in order to generalize on what the user will likein the future 306. Features about the clothing articles serve to assistin the generalization 308. These features may include color, texture,type of article, time since last worn, patterns in the clothing, andother items. The machine learning methods may also generalize from datafrom people who are not the current user 310, by associating similarpeople with similar fashion sense to the user and imputing that whatthose people like will be similar to what the current user likes. Themachine learning methods may use a numeric rating 312 or also word-baseddescriptions of how well articles of clothing go together rather thannumeric ratings 314. For example, when using fuzzy logic, the app mayoffer that a particular pair of pants goes with a shirt “pretty well” or“poorly.” The machine learning algorithms may also adjust the ratings ofclothes that are believed to go well together based on feedback from theuser and/or other users 316, taking into account fashion/color trends aswell as geographic and social dynamics.

In a preferred embodiment, the machine learning methods may also be usedto make suggestions to a user as to what to include in their virtualcloset based on data provided by retailers and/or other users of the app318. The machine learning methods accept data on current sales trends ofdifferent articles of clothing to users 320 that are generalized to besimilar to the current user and constructs a mathematical model 322 thatresults in suggesting which new articles of clothes the current usershould acquire. This could be for the purpose of completing an existingoutfit, such as having the app suggest that a particular tie would aptlycomplement a particular shirt, pants, belt, socks, and shoes alreadysaved by the user as an outfit. It could also be for the purpose ofsuggesting new outfits or individual articles of clothing to the usernot based on what the user already has in his/her virtual closet.

In a preferred embodiment, the app allows for users to contact otherusers to communicate 400 and identify items of clothing that could beshared, borrowed, or sold from one user to another 402. The app providesfor a user to view another user's virtual closet 404, with permission,and to identify articles of clothing that would be desired. The app alsoallows for a user to identify which articles of clothing would beavailable for other users 406.

In a preferred embodiment, the app also provides for collecting dataregarding the use of different articles of clothing 500, the purchasinghabits of users 502, and associated demographic information 504, whichcan be provided to clothing retailers and manufacturers 506 so as toimprove their business performance. Machine learning methods can also beused on these data to provide business insights to retailers andmanufacturers.

In a preferred embodiment, the user takes photos and/or video ofclothing items 600 from his/her closet and uses these photos to build avirtual closet 602. To do this, the app relies on wireframes and/orimages of articles of clothing that the user can identify 604. The photois then mapped to the wireframe, providing an easy method of presentinga particular item to the user for future use. As the light that isavailable for taking a photo of a clothing article is not anticipated tobe the same for all users at all times, after the user takes a photo ofan article of clothing, the photo is presented to the user to confirmthat it appears suitable 606. In addition, the user can touch a spot onthe photo that best represents the color of the article of clothing.This color is retained by the app in terms of its red-green-blue (RGB)values. These values can then be adjusted based on available informationabout lighting that was used to take the photo.

In a preferred embodiment one of several processes are used to increasethe rate that articles of clothing can be imported into a virtualcloset. In a standard approach as described in the parent patent, a usertakes individual photos of each individual article of clothing in thewardrobe and then imports these into a virtual closet. In anotherapproach, a user arranges articles of clothing from a wardrobe as anoutfit and takes one photo of a complete outfit for purpose of input tothe virtual closet and/or scoring. In another approach, a user takes onephoto of an entire wardrobe and uses a process of machine learning toautomatically identify and label the types of clothing in the wardrobe.This process can be thought of as the instantaneous capturing of allpossible outfits through one photo. In a final approach, a user can takevideo of the articles of clothing in their wardrobe as a “bulk import”and then divide the video content into useful frames of interest focusedon articles of clothing as a separate second step.

In the above method focused on the capture of an entire wardrobe throughone photo, there are of course many issues to contend with such as theappropriate identification of articles of clothing while viewing thearticle of clothing largely from the side as it hangs on a hangar.Further, in any user's wardrobe it is not always the case that clothingare grouped nicely by type. Typically, when someone takes a photo of anarticle of clothing to store in a virtual closet within a smartphoneapp, the article of clothing is either worn or is placed on anothersurface, such as a bed. To address the case where multiple items arephotographed at once, here we first consider the situation of taking aphoto of a person wearing clothes, such as a photo taken in a mirror, orhaving the items placed on a bed. This leads to a problem of classifyingobjects relative to the background of the color of that bed.

Various approaches exist in the literature for the clustering of partsof an image based on contrast relative to a background (Chen et al.,2019; Jaiswal et al., 2019; Watkins and van Niekerk, 2019). Additionallythere are proven edge-detection functions in the literature oftenincorporating extended Sobel methods, Canny, Prewitt, Roberts and othermethods (including fuzzy logic) (see Vincent and Folorunso, 2009). Herewe modify these for the automatic detection of articles of clothing fromone image such that a virtual closet can be populated rapidly, alongwith information about the type of clothing being added to the virtualcloset.

Convolutional neural networks (CNNs) are well suited to such problems.As extensions of multilayer perceptrons (fully-connected neuralnetworks), CNNs were inspired by the organization of the visual cortex.Input neurons respond to stimuli in segmented regions of the visualfield. This can be replicated in software where different areas of animage are processed by small parts of a larger neural network, whichprovides subsequent processing to arrive at a characterization of theimage (or the data presented if not from an image).

Given a corpus of training examples (e.g., different combinations ofarticles of clothing in different wardrobes) it is possible to train andtest CNNs to detect the location of articles of clothing in images.Given a corpus of training examples (e.g., different articles ofclothing assigned by their type in wardrobe settings) it is possible totrain and test CNNs to classify clothing by type. For instance Bhatnagaret al. (2017) studied the use of CNNs on the Fashion-MNIST database offashion items (see Xiao et al.) classified into 10 categories.Classification accuracy reached approximately 92% in test sets; however,the images in the database are much easier than in real-worldconditions. While Fashion-MNIST was an important contribution to thegeneral problem of classifying fashion images, the dataset is entirelygreyscale with each article of clothing laid out perfectly, not arrangedas found typically in a closet. Liu et al. (2016) assembled a colordatabase of articles of clothing on individuals with 120,000 images andstudied the use of a cascade of CNNs to identify positions of functionalkey points defined on the fashion items, such as the corners ofneckline, hemline, and cuff. The objective was to recall stored imagesfrom limited information. This line of research was an importantcontribution in a new direction, even though the result showed thatimages could be retrieved at best about 60% of the time using keylocations on specific garments.

A preferred embodiment for this approach makes use of machine learningapproaches such as CNNs or evolved neural networks to automaticallydetect articles of clothing in a single wardrobe photo and automaticallyassign each article of clothing to a type and/or color for purpose ofrapid addition to a virtual closet.

In a separate method for bulk import through video capture, there areother issues to contend with that are different from simple photography.For instance, the camera or smartphone must be pointed at a wardrobewhile the user exposes each article of clothing hanging in the wardrobein an orientation and lighting appropriate to the camera such that amajority of each article of clothing is in clear view during the video.Following this data capture of part of or an entire wardrobe by a user,the completed video is further annotated by one or more additionalmethods as described below.

Method 1 annotates the video using human intelligence (e.g., a processsuch as Mechanical Turk) whereby each clothing item is timestamped. Thetime stamp information of the video is used to train systems to identifya frame for each clothing item. Method 2 annotates the video usingmachine intelligence to recognize when a clothing item is displayedrelative to the motion of items or other elements in the video. Forinstance, as the user will be moving from one item to the next in thewardrobe, a machine learning approach recognizes that time stamp in thevideo with less motion likely represent articles of clothing to becategorized. Either of these approaches results in a model that istrained to recognize the timing of articles of clothing in a videostream. Once such a model has been developed a user-collected video canbe run through the model where the output of the model provides the timeand still frame for each article of clothing in the video.

This rapid categorizing of articles of clothing and their associatedstill frames abstracted via video capture generates an output that issuitable as input to other downstream models for backgroundsegmentation, identification of articles of clothing by type (e.g.,pants vs. shirts), identification of clothing categories (e.g., formal,work, leisure), identification of clothing features (e.g., pocket vs. nopocket), identification of patterns of interest (e.g., plaid, striped),identification of texture (e.g., smooth, rough). The importance of theabove video capture approach is in the time savings generated throughthe bulk import of articles of clothes from a user wardrobe.

As similar to our preferred embodiment for individual photo analysis,CNNs are used to determine the type and categorization of the garment ineach frame. The success of CNNs across a wide range of other examples ofvideo-event detection/classification provides a foundation for applyingsimilar technology to the problem of detecting and classifying clothingfrom a video. The technical approach for video processing relies ondetermining when the image is paused, finding the edges of the articleof clothing through a process of edge detection, and then proceedinganalogously to classifying a clothing item as if it were taken as astill photo. Therefore, the identification and assignment of garmenttype or set of garments uses at least one computational approachincluding, but not limited to, background segmentation, generation ofstill frames, video-event detection or edge detection using machinelearning in order to assist the individual with the task of selecting apreferred matching set of garments.

In a preferred embodiment, the user takes photos of clothing items 600from his/her closet and uses these photos to build a virtual closet 602.To do this, the app relies on wireframes of articles of clothing thatthe user can identify 604. The photo is then mapped to the wireframe,providing an easy method of presenting a particular item to the user forfuture use. As the light that is available for taking a photo of aclothing article is not anticipated to be the same for all users at alltimes, after the user takes a photo of an article of clothing, the photois presented to the user to confirm that it appears suitable 606. Inaddition, the user can touch a spot on the photo that best representsthe color of the article of clothing. This color is retained by the appin terms of its red-green-blue (RGB) values. These values can then beadjusted based on available information about lighting that was used totake the photo.

In a preferred embodiment, one method for making this adjustment relieson using a light meter to determine the luminosity in the area 608.Based on the luminosity as compared to a standard reference luminosity,the RGB values area adjusted up or down as appropriate to approximatewhat the RGB values would be had the photo been taken under a standardreference luminosity. In a preferred embodiment, another method formaking this adjustment relies on having the user take a photo undertheir current light of a reference material of a known color, such as awhite piece of paper 610. The resulting RGB values are then compared tothe known RGB values of the reference material, such as 255-255-255 forwhite. The app then makes adjustments to increase or decrease the RGBvalues as appropriate based on how much discrepancy there was betweenthe identified RGB values and known reference values.

It is recognized that the human eye can differentiate over 10 millioncolors; however, it would be impractical for a system, methods, anddevice in this application area to identify articles of clothing basedon 10 million colors. People are used to identifying colors based on avery small subset of all possible colors, and retailers often use astandard subset of clothes that are known by their color such as “khaki”pants. In a preferred embodiment, a method is used to map various colors700 to more commonly known colors 702 that are representative of thecolor being viewed. A set of colors, such as red, orange, yellow, lightgreen, dark green, light brown, dark brown, khaki, charcoal, light grey,black, white, ivory, light blue, navy blue, royal blue, light pink, darkpink, purple, lavender, and gold, that provides an intuitive spectrum ofpossible colors may be used. Colors are mapped to the colors in theselected set based on a distance measure, such as Euclidean distance inRGB space, and the closest color in the selected set is chosen as therepresentative color label to identify the color of the article ofclothing to the user.

In a preferred embodiment, the user may also decide to add labels forcolors that are of interest 704. For example, if the provided set oflabeled colors includes orange, but no other variations of orange, andthe user has a variety of orange-colored shirts, he may wish to addlabels for colors such as tangerine, rust, or pumpkin. This may be doneby inputting the RGB values associated with the color label, which aregenerally available from many sources.

In a preferred embodiment, users 800 share their virtual closets 802with garment cleaning companies 804 when dropping off their garments forcleaning. Given garment cleaners often already tag individual garmentswith barcodes and hand enter data about each garment through apoint-of-sale software system, as garments are entered into the garmentcleaner system, the data can be used to automatically populate theuser's virtual closet with garments 806 as a service. Given it is thecase that not all humans visualize colors in the same way, this dataentry can include errors based on the variance of human perception andthe subjectivity of color assignment. The methods of the presentinvention avoid these pitfalls by standardizing the color labeling onthe basis of objective, repeatable elements such as RGB values or theassignment of RGB values to color names. The preferred method alsoenhances the ability of garment cleaning companies to hire employeeswith disabilities through improved automation. Through our method thedata entry may also be accomplished through bulk import 808 of a singleuser's garments to speed up the process to help, for example, a drycleaner keep track of these garments while in their care. In thisrespect, the third-party entity's virtual closet 810 contains garmentsfrom a variety of users and where these garments come and go from thethird-party entity's virtual closet with time. Once garments are in thesystem, the garment cleaner can use the method for garment tracking 812through the cleaning process from start to finish while sending outbounduser notifications 814 regarding the status of the process and when eachgarment or collection of garments is ready for pickup. From the user'sperspective, garments that are at the garment cleaners still appear inthe individual user's virtual closet but with a label that indicatestheir status in the garment cleaning process.

Users can choose to make their personal calendars 816 available to thegarment cleaner. In this embodiment, for example, a dry cleaner learnswhen to expect to receive user garments after a trip or when to providecleaned garments back to the customer in time for a subsequent trip. Themethod also allows garment cleaners to appropriate time discounts orother marketing offers to customers relative to their calendar or knownhistory of travel or previous history of garment cleaning. The methodcan also allow garment cleaners to improve their understanding ofpossible service offerings including on-demand pickup of garments ordrop-off facilities that improve access to garment cleaning services.

As a part of the present invention, dry cleaners can monitor thedeterioration of garments 818 and number of wash cycles on a per-garmentbasis, adding this information back to the closet for each individualuser. This information will help the users forecast when it is time toeither repair or replace a garment of specific type and understand howoften specific garments are used and cleaned. Garments that are agingand yet routine use by a user are strong candidates for replacement, andlinks can be provided directly to a user that connects the user with aretailer with similar item for sale thus helping to reduce the timerequired for users to find replacement garments and helping directcustomers to retailers.

A preferred embodiment utilizes garment cleaners and their informationacross multiple user virtual closets 820 as a hub for the renting orsharing or sale of garments 822 between customers. For instance, if userA has a jacket that is now ready for pickup, user is A alerted by a drycleaner. User A can choose to also make the jacket available for rent.In this case, the dry cleaner can make the jacket available in thevirtual closets of other users of the system. If User B wishes to rentUser A's jacket, this can be accomplished through the dry cleaner, withUser B picking up the jacket for use with payment either directly toUser A or payment to the dry cleaner on behalf of User A. When rented,that garment appears as “rented” in User A's virtual closet until suchtime as User B has completed their use. When User B has completed use,User B returns the rented garment to the same dry cleaner for cleaningafter which User A is alerted again that the garment is available forpickup or rent. This process is repeated until User A collects thegarment physically from the dry cleaner or until the dry cleanerrequests that User A does so or pays a fee for storage with the drycleaner. Throughout the process of rental, the virtual closets of bothUser A and User B are updated with regards to the status of the jacket824. This process can be repeated for any garment or accessory suitablefor dry cleaning irrespective of the age or gender of either user. Notethat the standardization of colors across all garments and across alluser virtual closets means that when User B expects to receive a jacketof a specific color from User A, the jacket will indeed be that colorand not some variation based on the subjective color assignment ofeither User A or User B, one or both of whom might have issues withcolor perception such as those with color blindness. Use of our methodeliminates this subjectivity and ensures that all colors across allvirtual closets are represented in a standardized way.

The method allows for the information regarding the entire set ofgarments being processed through a garment cleaning store over time tobe examined for trends regarding the garment age, producer, size, color,pattern, or other characteristics. For example, such trend analysis 826is useful for dry cleaning stores to better understand the nature oftheir customers, for load and schedule balancing, and for possiblediscounts back to specific users and specific times to improve revenue.Such information is also licensed back to retailers to provide newinsights across broad geographic regions and across garment retailershelping drive new product decisions and providing direct access to usersthrough their virtual closets. Users can opt in to have retailersconnect with them directly regarding product offerings or reducedpricing on garments in light of the user's garments, virtual closet, andcleaning trends.

Given point of sale systems are key in the garment cleaning industry,the method can be connected directly to point of sale systems to furtherimprove quality of customer experience and business efficiency or themethod can be on an individual basis on each user's smart phone.

In a preferred embodiment, the user maintains a presence in both a real,physical environment 900 as well as in virtual environments 902. Thisduality between both real and virtual environments is becomingincreasingly important as more people spend time interacting in digitalcomputing worlds. A user in a physical environment makes use of asoftware application 904 to assist with colors and choices of garmentsto wear via a fashion recommendation system. As previously describedsuch a system converts the garments in a physical closet 906 intodigital representations of those garments in a virtual closet 908. Thedigital renditions 910 of these garments have their own utility in awide range of contexts. For instance, using color matching 912approaches and the knowledge of many garments, their colors, associatedattributes, and use, a design engine 914 can be used to create differenttypes of digital garment representations. For example, these can takethe form of non-fungible tokens 916 representing unique and novelgarment colors and/or attributes, or wholly new types of digitalgarments and their color associations 918, or even digital versions ofpopular garments worn by people in reality 920. Each of these can beused in their own way to add value to a database of digital garmentdesigns 922 that can be provided to customers such as designers andretailers 924, or other end users for income either on their own asdigital representations such as in the case of non-fungible tokens orwith the specific intent of converting the digital renditions back intogarments that can be worn in reality 926. Further the garmentsrepresenting the output from the design engine 914, such as non-fungibletoken designs 916, novel digital garments 918, or digital versions ofpopular garments 920, can be provided to a digital environment such as adigital gaming environment 928 such that the garments can be applied tovirtual characters 930 in said digital environments. The digitalrenditions 910 of garments in the user's virtual closet can also beapplied to virtual characters in these gaming or other digital worlds,if the user, for instance, would choose to dress like they would inreality. The garments, their colors, and attributes, can be evaluated indigital contexts 932 to determine which are considered more or lessdesirable. Garments can be assigned value as desired 934 or undesired936 in specific contexts. These garments and their assigned values canbe added to the database of digital garment designs 922 and used as apart of the decision making for designers and retailers 924 in theirdecision of whether or not to generate physically wearable garments fromthe set of desired digital garments in light of similar contexts betweenthe virtual and real worlds. The set of garments derived from digitalenvironments and converted back into physically wearable garments forsale or license to consumers and once attained, these can be added tothe user's physical closet 906.

This method has specific advantages. Artists can use the method togenerate new garments with novel color combinations and try these indigital environments for more rapid feedback. Users can express greaterpersonality by comparing the utility of the digital representations oftheir real-world garments versus digital representations of garmentsthey have never worn or would be unable to afford in real contexts. Thedesign engine itself represents a new opportunity for the fashioncommunity to generate new garments and outfits and could accentuate oreven replace human designers. And those users who have visionimpairments such as are color blindness or complete blindness can knowthat their digital representations are appropriate facsimiles of thecolors and garments they would wear in real-world contexts.

In a preferred embodiment the design engine 1000 makes use of machinelearning techniques, which may include a combination of neural networksor deep learning (neural networks with more than one hidden layer),evolutionary algorithms, reinforcement learning, support vectormachines, random forest methods, swarm optimization, fuzzy logic, andother techniques. As with the color matching approaches describedpreviously for the generation of outfits within the softwareapplication, machine learning methods use data regarding the digitalrepresentations of garments, their desirability in digital contexts inorder to generalize what will work well in future digital environments1002. This approach can be used to generate novel digital garmentrepresentations that are well-suited to digital environments where thedigital garment could if desired be generated into a novel real garmentfor use in real-world environments that are similar to the digitalenvironment. For instance, a colorful garment could be designed for awinter setting in a digital gaming environment and if that wasdetermined to be of utility in that context, the same digital garmentrepresentation could be provided to a garment maker and generated in thereal-world for use in winter settings. The method makes use of thedigital environment for the generation and assay of a wide variety ofgarment types, colors, and accessories in order to discover which ofthose garment types, colors, and accessories have particular utility indigital environments such that knowledge of their utility can beidentified at low cost and in a way that could be used to generate realphysical instantiations of the digital garment representations ifdesired. Given the input to the design engine includes the garments andtheir attributes this includes their color. A preferred use of the abovedesign engine is the design and evaluation of garments in digitalsettings for color deficient persons such that the garments they use indigital environments can help overcome their deficiencies and such thatif a useful set of colors, patterns or colors and patterns is identifiedas being useful to color deficient persons in those digital settings,the same colors and patterns could be used on garments in real-worldsettings to improve their quality of life. As noted previously in thispatent, the color in the software application is retained in terms ofits RGB values as these are adjusted based on available lighting andprovided a color name. The design engine can make use of this RGB-basedcolor palette 1006 or other color palettes such as the Pantone colorpalette 1008 or combinations of these palettes for the colors andassignments used by the design engine to generate novel digitalrepresentations.

In a preferred embodiment, users 1100 upload not only their garmentphotos 1102 to the virtual closet 1104 but also any makeup 1106 thatthey use as well as a photo of the user's face 1108. All of these inputsto the virtual closet are then associated with RGB values and provided acolor name. In terms of makeup, these can be associated with a colorpalette 1110 such as an RGB-based color palette or the Pantone colorpalette or combinations of these palettes or palettes specific to thetype of makeup being used such as more granularly defined shades of redwhen working with lipstick. Similarly, the color of the user's face isidentified in terms of RGB values and associated with a color name witha color palette specific to the distribution of human skin tones 1112.Other facial colors 1114 are captured including eye color, hair color,eyebrow color, etc. each relative to the RGB-based color palette orother color palette specific to the distribution of human skin tones.All of this information can be stored in the user's virtual closet 1104within a computing device 1116. As described previously, a central focusof this invention is the color matching of garments of different types.However, that matching can be accentuated or even driven by colormatching to makeup, natural skin tones, or other colors associated witha user's face. The objective of this preferred embodiment is to matchgarments not only with respect to their colors but to the colorsassociated with the user's face or to match the colors associated with auser's face with the colors of their chosen garments. Within thecomputing device, while it is possible for a user to use their finger toidentify the colors of specific locations on each image, a preferredembodiment for the facial color components uses method of machinelearning to automatically recognize the locations of facial features1118 such as eyes, eyebrows, and hair and then automatically generatetheir associated RGB values. In a preferred embodiment the generation ofoutfits 1120 focuses not only on garments and their associated colorsbut also makeup and their associated colors as well as facial colorsincluding skin tone. Further, in the generation of outfits, the user cantake into consideration other fashion elements 1122 such as the currentseason, a venue or location, or fashion trends. In a preferredembodiment this combination of garment colors and facial colors is keyin the generation and scoring 1124 of outfits for their quality. Outfitscan be scored in several regards. As noted previously it is possible toscore combinations of garments with regards to their color matching. Itis also possible to score combinations of makeup with regards to theircolor matching. Further it is also possible to score combinations ofboth garments and makeup with regards to color matching and also do sorelative to the user's skin tone. Such matching improves the overalllook of a person who is interested in matching colors in the best waypossible.

As the user generates outfits that include both garments, makeup, andskin tone, a database of outfit information 1126 can be generated thatincludes information about the garments, makeup, facial features, skintone and associated seasons, venues, or fashion trends. In the eventthat the user is generating outfits in a virtual context such as asimulated dating game, a similar database of digital outfit information1128 can be generated that includes information about the garments,makeup, facial features, skin tone, and associated seasons, venues, orfashion trends in those virtual environments. Given sufficient outfitsand their scores in either real or virtual environments or both, it ispossible to use machine learning to assist the user in the tailoring ofsuggested new garments and/or makeup choices 1130 that would improvetheir score or otherwise match their personal color style over time.Over the set of users, machine learning is used to improve theunderstanding of fashion trends 1132 in the matching of garments tomakeup relative to skin tones in either or both of these settings.

As noted previously, in a preferred embodiment the user maintains apresence in both a real, physical environment as well as in virtualenvironments. It is the case that the system and methods above for thematching of colors across garments, makeup, and skin tone also can applyto virtual environments. For example, in a virtual context a player maywish to make sure that their character matches in terms of the color oftheir garments as well as the makeup on their face before interacting ina simulated dating environment. The virtual contexts provide additionalopportunity for scoring of garment, makeup, and skin tone combinationsrelative to the performance in those virtual environments. Further,combinations of garments, makeup, and skin tone that are of high qualityin virtual environments can be either shared with other users of thecomputer application or used to improve the suggested combinationswithin said computer application. Further, these combinations,considered as the equivalent of a “look” in the fashion world, can besaved and sold as their own virtual art. It can be as well that newmakeup colors could be derived in a personal basis for users of specificskin tones and garment colors in their virtual closet.

It is to be expected that the description of the preferred embodiment isnot a limitation on variations or extensions of the invention. Forexample, it may be desirable to allow a male user to create an outfitstarting with an article of clothing other than a shirt.

This invention described here is useful for optimizing color or fashiondecisions for people with or without visual impairment. The inventioncouples human expertise of many different types and the user'scollection of garments and accessories to recommend clothing sets thatare appropriate in light of work, season, weather, or other environmentrequirements. Using the system and device the user then uses a processof iterative evolution to arrive at garment sets that are best forfuture events and in light of many constraints.

It will be appreciated that details of the foregoing embodiments, givenfor purposes of illustration, are not to be construed as limiting thescope of this invention. Although several embodiments of this inventionhave been described in detail above, those skilled in the art willreadily appreciate that many modifications are possible in the exemplaryembodiments without materially departing from the novel teachings andadvantages of this invention. Accordingly, all such modifications areintended to be included within the scope of this invention, which isdefined in the following claims and all equivalents thereto. Further, itis recognized that many embodiments may be conceived that do not achieveall of the advantages of some embodiments, particularly of the preferredembodiments, yet the absence of a particular advantage shall not beconstrued to necessarily mean that such an embodiment is outside thescope of the present invention.

We claim:
 1. A method for assisting an individual with the task ofselecting a preferred matching set of garments to assemble an outfit forthe individual, the method comprising: providing a data storage systemfor storing digital renditions of garments; providing a portablecommunication device to extract color and/or pattern from garmentsthrough use of a camera and at least one algorithm; providing aprocessor capable of accessing locally stored and/or remote informationabout or learning the preferred matching set of garments; assigning eachgarment in the set of garments a red-green-blue (RGB) value; providing asuitability ranking for matching compatibility of the garment or the setof garments; and providing recommendations for preferred matchinggarment or set of garments by organizing the garments in at least onequeue selected from the group consisting of audial, tactile, visual or acombination thereof, wherein the individual can touch a spot on thedigital renditions of the garments or accessories to determine its RGBvalue, further wherein the RGB value associated with a particulargarment or accessory is mapped to exemplar RGB values associated withspecified colors, wherein the individual imports garments or set ofgarments, through a series of photos or video, for bulk import into avirtual closet, wherein the virtual closet is used to create a databaseof digital garment designs together with color matching comprising atleast one selected from the group consisting of makeup, facial featuresand skin tones.
 2. The method of claim 1 where quality associated withmatching of clothing is represented to the individual through audiofeedback in the form of a tone or voice.
 3. The method of claim 1 wherequality associated with matching of clothing is represented to theindividual through vibration or tactile sensation.
 4. The method ofclaim 1 where quality associated with matching of clothing isrepresented to the individual numerically.
 5. The method of claim 1where quality associated with matching of clothing is represented to theindividual using language.
 6. The method of claim 1 where garments areassociated with an RGB value that is most representative of the garment.7. The method of claim 1 wherein the algorithm is a case-basedprocedure.
 8. The method of claim 1 wherein data are collected from asocial network of individuals.
 9. The method of claim 1 wherein thegarments or set of garments are inputted to a software application usinga process of bulk import via photography or video.
 10. The method ofclaim 1 where photos of garments, makeup, facial features, and skintones are provided to a virtual closet.
 11. The method of claim 1 wheregarments, makeup, facial features, and skin tones are associated withcolor palettes appropriate to the range and granularity of colors withineach of those domains.
 12. The method of claim 1 where colorcombinations of garments, makeup, and facial features are evaluated fortheir quality.
 13. The method of claim 1 where machine learningtechniques are used to automatically identify the locations and colorsassociated with facial features.
 14. The method of claim 13 where themachine learning techniques include a combination of neural networks ordeep learning, evolutionary algorithms, reinforcement learning, supportvector machines, random forest methods, swarm optimization and fuzzylogic.
 15. The method of claim 1 where a database of outfit informationis generated including information about garments, makeup, facialfeatures, skin tone, and associated seasons, venues, or fashion trends.16. The method of claim 15 where machine learning is used to derivesuggested garment and/or makeup choices in light of facial features,skin tone, and associated seasons, venues, or fashion trends to improvea user's color style over time.
 17. The method of claim 16 where themachine learning techniques include a combination of neural networks ordeep learning, evolutionary algorithms, reinforcement learning, supportvector machines, random forest methods, swarm optimization and fuzzylogic.
 18. The method of claim 1 wherein a database of digital outfitinformation is generated from digital environments in at least onemanner selected from the group consisting of populating a design engine,creating a digital rendition of garments and developing novel colorpatterns for use in new digital garments.
 19. The method of claim 18where the database of digital outfit information is populated withinformation about garments, makeup, facial features, skin tone, andassociated seasons, venues, or fashion trends.